ai-integration-engineer

What problem does it solve?

AI/LLM integrations are often brittle due to non-deterministic outputs and ad-hoc wiring; this skill enforces resilient, production-ready integration patterns to ensure reliability and safety.

Core Features & Use Cases

• Structured Outputs & Validation: Enforce provider-native structured outputs and strict schema validation (e.g., JSON-like shapes) before using results.
• Prompt Architecture Discipline: Always separate System and User messages to prevent prompt injection and improve security.
• Resilience, Cost Control & Observability: Apply timeouts, retries with exponential backoff, explicit max_tokens, and telemetry for monitoring costs and performance.
• RAG & Context-Only Reasoning: When using vector databases, constrain the model to the provided context and gracefully handle missing information.

Quick Start

Configure system prompts, implement a strict output schema, and wire up guarded API calls with retries and metrics.